Among the 26 roughness parameters described in ISO 25178 standard, the parameters used to characterize surface performance in characterization parameter set (CPS) lack scientificity and unity, resulting in application confusion. The current CPS comes from empirical selection or small sample experiments, thus featuring low generality. A new method for constructing CPS in rough surfaces is proposed to solve the above issues. Based on a data mining method, statistical theory, and roughness parameters definitions, the 26 roughness parameters are divided into CPS and redundant parameter sets (RPS) with the help of reconstructed surfaces and machining experiments, and the mapping relationships between CPS and RPS are established. The research shows that RPS accounts for 50%, and CPS, of great significance for surface performance, and has the ability to fully cover surface topography information. The birth of CPS provides an accurate parameter set for the subsequent study of different surface performance, and it provides more effective parameters for evaluating the workpiece surface performance from the same batch.

As microtopography can influence the contact behavior of materials, it is of great significance to study the correlation between morphology characterization parameters and contact performance. In the light of complex relevance of parameters, a method for screening roughness parameters (RP) to characterize contact performance is constructed to get the maximum influence parameters on the contact stress (CS) and avoid the error of experiential selection. First, Pearson's coefficient and back propagation (BP) neural network are utilized to elaborate on correlation level between RP and CS and to build the regression model. Then global sensitivity analysis (Sobol) and local sensitivity analysis (MIV and Garson) are introduced to demonstrate RP quantitative influences on CS and select main RP for characterizing contact performance. The research shows (1) in the correlation analysis, RP with high correlation and noncollinearity on σ_max are Sa, Sdq, S5p, Spk, and Svk; With regard to Mpmax and τ_max, Sa, S5p, Sdq, and Vmp are on display, (2) RP importance sequence based on the results of correlation analysis is Sa, Spk, Sdq, Svk, S5p for σ_max, and Sa, Vmp, Sdq, S5p for Mp_max and τ_max, and (3) For the comprehensive main parameters model, RP for characterizing contact performance under the three contact stresses are Sa, Spk, and Vmp, belonging to height parameter, function parameter, and volume parameter, respectively. According to definition, all of them can significantly affect the stress concentration and distribution on contact surface of materials, which validates the rationality of the method.

Roughness surfaces contact analysis is cutting-edge research in interface design. The 3D rough surface amplitude distribution characterized by height distribution parameters( Sq (root mean square), Ssk (skewness), Sku (kurtosis)) has a great influence on the extreme value and distribution of the interface contact stress. However, the relationship between height distribution parameters and surface maximum mises stress ( σ _max ) is still unclear and lacks of in-depth study. Through BP(Back Propagation) neural network, global sensitivity qualitative ( Morris ) and quantitative ( Sobol ) analysis methods, the relationship between Sq , Ssk , Sku and σ _max under different loads is studied. Based on complete polynomial and permutation combination method, the optimal correlation model between height distribution parameters and σ _max was established, and particle swarm algorithm was introduced to analyze σ _max extreme values under different Sq . The results show that: (1) Under different loads, the order about height distribution parameters influence on surface contact stress is: Sq   > Ssk   > Sku , and as the load increases, the influence of Ssk and Sku gradually decreases. (2) In different roughness surfaces, the influence of Ssk and Sku on the contact performance is significantly full of discrepancy. The research results provide reference and technical support for active design of rough surface microstructure to improved contact performance.

With surface roughness restricted by grinding parameters, the characterization of roughness parameters and the inversion of grinding parameters are of great significance for improving surface performance and realizing active surface machining. This research proposes a combination of statistical theory and data-driven analysis to solve the above problems. Pearson correlation analysis and multivariate variance analysis indicate the correlation characterization parameter set (CPS) consists of Sa, Vmp, Vvv, and Sz and that there are differences in the influence of grinding parameters on the parameters in CPS. Adjustment of support vector machine (SVM) core parameters makes it possible to construct expansion parameter set (EPS) optimal inversion models. By designing pseudo-surface random roughness parameters and grinding experiments, the reliability of inversion models is verified. The results show: (1) The better generalization of inversion model indicates skewness Ssk and kurtosis Sku in EPS have important implications for the optimal inversion model and surface characterization and (2) The data-driven model based on support vector machine provides machining guidance for obtaining the expected ultrasonic grinding surface.

Shot peening for semi-finished or finished parts is a high-performance precision manufacturing process, and the final micro-surface morphology has layered superposition features, that is, the original surface features and shot peening features coexist on the part surface. The existing layered superposition theory is based on the idea that the surface is superimposed under different baselines, but it cannot characterize the two-process surface where the characteristic baseline cannot be separated after shot peening. To solve this problem, a new layered surface superposition modeling method is proposed in this paper with the grinding-shot peening surface as the research object. In this method, the main texture features of the grinding surface are extracted by B-spline surface fitting, and the spatial distribution of craters in the shot peening surface is adjusted by this texture feature. The initial profile of the grinding-shot peening surface is constructed according to the Pawlus superposition theory, and its height parameters are adjusted by the time-frequency iteration method. In this paper, the modeling method is verified by the measured grinding-shot peening surfaces with a coverage of 100% and 200%. The results show that this method can not only ensure the uniformity of crater texture on the reconstructed surface, but also ensure that the maximum error of spatial parameters is less than 2%. Moreover, the symmetrical autocorrelation functions of the reconstructed surface are consistent with the characteristics of the measured surface.

To reveal the relationship between rough tooth surface microstructure characteristics and contact performance, contact analysis is carried out based on the measured surface topography of the tooth surface of ultrasonic grinding by utilizing the elastic-plastic contact mechanics and statistical correlation analysis theory. Simple correlation analysis and partial correlation analysis are adopted for the gear three-dimensional surface roughness parameters and the maximum Mises stress and maximum shear stress. Then, the method of stepwise regression analysis and path analysis is used to construct the best fitting linear model of 3D roughness parameters and the maximum Mises stress and maximum shear stress, and the parameters’ decision coefficient is obtained. The research shows: (1) the rank of comprehensive influence factors of the maximum Mises stress is as follows: arithmetical mean height (Sa), peak material portion (Smr1), maximum peak height (Sp), reduced peak height (Spk), and minimum curvature radius and height ratio (K), where Smr1, Spk, and K are negatively correlated with the maximum Mises stress; (2) the comprehensive influence variables of the maximum shear stress are in the order of Sa, Spk, and Vmp, among which Spk and Vmp are inversely related to the maximum shear stress.